Accurate channel estimation is essential for massive multiple-input multiple-output (MIMO) technologies in next-generation wireless communications. Recently, the radio radiance field (RRF) has emerged as a promising approach for wireless channel modeling, offering a comprehensive spatial representation of channels based on environmental geometry. State-of-the-art RRF reconstruction methods, such as RF-3DGS, can render channel parameters, including gain, angle of arrival, angle of departure, and delay, within milliseconds. However, creating the required 3D environment typically demands precise measurements and advanced computer vision techniques, limiting accessibility. This paper introduces a locally deployable tool that simplifies 3D environment creation for RRF reconstruction. The system combines finetuned language models, generative 3D modeling frameworks, and Blender integration to enable intuitive, chat-based scene design. Specifically, T5-mini is finetuned for parsing user commands, while all-MiniLM-L6-v2 supports semantic retrieval from a local object library. For model generation, LLaMA-Mesh provides fast mesh creation, and Shap-E delivers high-quality outputs. A custom Blender export plugin ensures compatibility with the RF-3DGS pipeline. We demonstrate the tool by constructing 3D models of the NIST lobby and the UW-Madison wireless lab, followed by corresponding RRF reconstructions. This approach significantly reduces modeling complexity, enhancing the usability of RRF for wireless research and spectrum planning.

翻译：精确的信道估计对于下一代无线通信中的大规模多输入多输出（MIMO）技术至关重要。近年来，无线电辐射场（RRF）作为一种有前景的无线信道建模方法出现，它基于环境几何结构提供了信道的全面空间表征。最先进的RRF重建方法，例如RF-3DGS，能够在毫秒级时间内渲染信道参数，包括增益、到达角、出发角和时延。然而，创建所需的三维环境通常需要精确的测量和先进的计算机视觉技术，这限制了其可及性。本文介绍了一种可本地部署的工具，该工具简化了用于RRF重建的三维环境创建过程。该系统结合了微调的语言模型、生成式三维建模框架以及Blender集成，以实现直观的、基于聊天的场景设计。具体而言，我们微调了T5-mini用于解析用户指令，同时使用all-MiniLM-L6-v2支持从本地物体库中进行语义检索。在模型生成方面，LLaMA-Mesh提供快速的网格创建，而Shap-E则提供高质量的输出。一个定制的Blender导出插件确保了与RF-3DGS流程的兼容性。我们通过构建NIST大厅和UW-Madison无线实验室的三维模型，并随后进行相应的RRF重建，展示了该工具的功能。此方法显著降低了建模复杂度，增强了RRF在无线研究和频谱规划中的可用性。